{"id":"W2142186549","doi":"10.1186/gb-2013-14-10-r117","title":"Sequence signatures extracted from proximal promoters can be used to predict distal enhancers","year":2013,"lang":"en","type":"article","venue":"Genome biology","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; U.S. National Library of Medicine; Amgen; Simons Foundation Autism Research Initiative; National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of Neurological Disorders and Stroke; National Institute of General Medical Sciences; National Human Genome Research Institute; Canadian Institutes of Health Research; National Institutes of Health; Simons Foundation; University of Chicago","keywords":"Enhancer; Promoter; Biology; Gene; Genetics; Transcription factor; Gene expression; Computational biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004105211,0.0003994208,0.0003436622,0.001223865,0.0001348622,0.0002756439,0.0002561632,0.0003551985,0.001054907],"category_scores_gemma":[0.00187925,0.0001951461,0.0003227942,0.000558415,0.0002159264,0.0003050615,0.0002294343,0.0004918569,0.0008870376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001826013,"about_ca_system_score_gemma":0.0002909992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003842737,"about_ca_topic_score_gemma":0.0006203339,"domain_scores_codex":[0.9997897,0.00002555825,0.00001715754,0.00007146761,0.00007190384,0.00002425816],"domain_scores_gemma":[0.9986382,0.0007150515,0.0002653462,0.00006945285,0.0002459311,0.00006592151],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008787566,0.0002164402,0.09431466,0.0006263227,0.00008645945,0.000289012,0.000199345,0.01610401,0.722553,0.0009803005,0.0007240508,0.1630276],"study_design_scores_gemma":[0.00006591446,0.0008915768,0.1780532,0.0001425206,0.0002474022,0.00146848,0.0001879529,0.292009,0.5123501,0.00647605,0.008026109,0.00008164639],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8485038,0.001011888,0.1447927,0.00009686686,0.00003161737,0.00007201912,0.002559976,0.001113671,0.001817371],"genre_scores_gemma":[0.9240889,0.0003485247,0.06943402,0.00004899525,0.00003374663,0.0001120265,0.00507193,0.0001134059,0.0007485661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001223865,"threshold_uncertainty_score":0.003529012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01049277131837765,"score_gpt":0.2324079007855935,"score_spread":0.2219151294672159,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}